{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据缺失时进行算法填充\n",
    "常用算法有向前填充和向后填充两种\n",
    "所谓向前填充是指用之前最近得一个数据对空值进行填充\n",
    "向后填充是指用之后最近得一个数据对空值进行填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B   C  D\n",
       "0  NaN  2.0 NaN  0\n",
       "1  3.0  4.0 NaN  1\n",
       "2  NaN  NaN NaN  5\n",
       "3  NaN  3.0 NaN  4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.DataFrame([[np.nan, 2, np.nan, 0], [3, 4, np.nan, 1], [np.nan, np.nan, np.nan, 5], [np.nan, 3, np.nan, 4]], columns = list('ABCD'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B   C  D\n",
       "0  NaN  2.0 NaN  0\n",
       "1  3.0  4.0 NaN  1\n",
       "2  3.0  4.0 NaN  5\n",
       "3  3.0  3.0 NaN  4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 向前填充\n",
    "df.fillna(method = 'ffill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "描述性统计的可视化分析\n",
    "1. 直方图：是以一种图形方法来概括给定数值X的分布情况的图示\n",
    "如果X是离散的变量，比如股票类型，则对于X的每个一直的值，画一个柱或者竖直条。条的高度标识该X值出现的频率，这种图更多被称为条形图\n",
    "如果X是连续型变量，比如股票的市盈率，更多的使用术语直方图。X的值域被划分成不相交的连续子域。子域称为桶或者箱。对于每个子域，画一个条，其高度表示在该子域观测到的计数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x199c6495278>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "data_n1 = np.random.normal(-1, 2, 1000)\n",
    "data_n2 = np.random.normal(0, 1, 5000)\n",
    "data_n1 = pd.DataFrame(data_n1, columns = ['normal'])\n",
    "data_n2 = pd.DataFrame(data_n2, columns = ['normal'])\n",
    "data_n1['normal'].plot.hist(bins = 30, figsize = (10, 6), alpha=0.5, density=True)\n",
    "data_n2['normal'].plot.hist(bins = 30, figsize = (10, 6), alpha =0.5, density = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "散点图是确定两个数值变量X，Y之间看上去是否存在联系，以及具有怎样的相关模式的图形方法之一。\n",
    "为构造散点图，将每个值对（X，Y）视为一个代数坐标对，并作为一个点画在平面上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x199c857d908>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " import seaborn as sns\n",
    "sns.set(style = 'ticks')\n",
    "\n",
    "# load the example dataset for Anscombe's quartet\n",
    "df = sns.load_dataset(\"anscombe\")\n",
    "\n",
    "# shwo the results fo a linear regression within each dataset\n",
    "sns.lmplot(x=\"x\", y=\"y\", col=\"dataset\", hue=\"dataset\", data=df, col_wrap=2,ci=None, palette = \"muted\", height=4, scatter_kws={\"s\":50, \"alpha\":1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "盒图\n",
    "为了更加全面的了解一个分布，五数概括是一个很好的工具。\n",
    "五数概括由中位数、两个四分位数、最小、最大值组成\n",
    "盒图（boxplot）是一种流行的分布的图形表示，体现了五数概括的特点：\n",
    "①盒的端点一般在四分位数上，使得盒的长度是四分位数极差（英文叫IQR）\n",
    "②中位数用盒内的线标记\n",
    "③盒外的两条线延申到最小和最大的观测值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style = \"ticks\", palette = \"pastel\")\n",
    "\n",
    "# load the example tips dataset\n",
    "tips = sns.load_dataset(\"tips\")\n",
    "\n",
    "# draw a nested boxplot to show bills by day and time\n",
    "sns.boxplot(x = \"day\", y = \"total_bill\", hue = \"smoker\", palette = [\"m\", \"g\"], data = tips)\n",
    "sns.despine(offset = 10, trim = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上代码是将day作为x轴，用smoker作为第二维度，画出了total_bill的盒图。最中间的线就是中位数，实体上下边缘是四分位数，上线影线是最大最小值，灰点是异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "也可以将第二维度选位sex，只需将函数中的参数hue换成sex即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x199c8bb3a20>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = \"day\", y = \"total_bill\", hue = \"sex\", palette = [\"m\", \"g\"], data = tips)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
